Are POS tagging, Chunking, Disambiguation, etc. subtasks of annotation?
The procedures you mention don't need to involve writing code. There are now many ready-made tools available which implement various algorithms. 

POS-Tagging, Chunking, and Semantic Tagging/Disambiguation are knowledge-based procedures which can all be seen as classification/clustering tasks: POS-Tagging and Disambiguation are classifications, in that they propose a label (a POS- or semantic tag) that is assigned to a token. Chunking is a clustering algorithm, as it finds coherent groups in sequences of tokens. You could also view it as segmentation, as it splits a sentence into parts.

In principle they don't have anything to do with machine learning; the algorithm you are using (ML or not) is an implementation detail. Early Taggers were mostly rule-based, but with increasing availability of annotated data it became more feasible to use ML algorithms. This, however, has got nothing to do with how you classify the procedures.

In general I would call the activity you describe as 'annotation', as you enrich the source text by adding descriptive categories to tokens and token sequences.

So, no, they do not have to involve programming or implementing algorithms, and you don't need to be technical to perform them. Though some knowledge of linguistic concepts would help.